{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:38:24Z","timestamp":1783438704520,"version":"3.54.6"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Healthcare has been fundamentally changed by the expansion of IoT, which enables advanced diagnostics and continuous monitoring of patients outside clinical settings. Frequently interconnected medical devices often encounter resource limitations and lack comprehensive security safeguards. Therefore, such devices are prone to intrusions, with DDoS attacks in particular threatening the integrity of vital infrastructure. To safe guard sensitive patient information and ensure the integrity and confidentiality of medical devices, this article explores the critical importance of robust security measures in healthcare IoT systems. In order to detect DDoS attacks in healthcare networks supported by WBSN-enabled IoT devices, we propose a hybrid detection model. The model utilizes the advantages of Long Short-Term Memory (LSTM) networks for modeling temporal dependencies in network traffic and Convolutional Neural Networks (CNNs) for extracting spatial features. The effectiveness of the model is demonstrated by simulation results on the CICDDoS2019 datasets, which indicate a detection accuracy of 99% and a loss of 0.05%, respectively. The evaluation results highlight the capability of the hybrid model to reliably detect potential anomalies, showing superior performance over leading contemporary methods in healthcare environments.<\/jats:p>","DOI":"10.3390\/fi18010052","type":"journal-article","created":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T12:31:41Z","timestamp":1768480301000},"page":"52","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Cyber Approach for DDoS Attack Detection Using Hybrid CNN-LSTM Model in IoT-Based Healthcare"],"prefix":"10.3390","volume":"18","author":[{"given":"Mbarka","family":"Belhaj Mohamed","sequence":"first","affiliation":[{"name":"Laboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), National School of Engineers of Gabes (ENIG), University of Sfax, Gabes 6029, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dalenda","family":"Bouzidi","sequence":"additional","affiliation":[{"name":"Laboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), National School of Engineers of Sfax (ENIS), University of Sfax, Sfax 3038, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manar","family":"Khalid Ibraheem","sequence":"additional","affiliation":[{"name":"Laboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), National School of Engineers of Sfax (ENIS), University of Sfax, Sfax 3038, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1239-0066","authenticated-orcid":false,"given":"Abdullah Ali Jawad","family":"Al-Abadi","sequence":"additional","affiliation":[{"name":"Laboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), National School of Engineers of Sfax (ENIS), University of Sfax, Sfax 3038, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3219-2371","authenticated-orcid":false,"given":"Ahmed","family":"Fakhfakh","sequence":"additional","affiliation":[{"name":"Laboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), National School of Electronics and Telecommunications of Sfax (ENET\u2019com), University of Sfax, Sfax 1163, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.ceh.2024.07.001","article-title":"Internet of Things in healthcare: An adaptive ethical framework for IoT in digital health","volume":"7","author":"Wakili","year":"2024","journal-title":"Clin. 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